What type of study is this?

This is a In Vitro Study study.

August 22, 2025Open Access

Application of Machine Learning and Data Augmentation Algorithms in the Discovery of Metal Hydrides for Hydrogen Storage

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Authors

GBGiovanni BeltramePolytechnique Montréal EDErika Michela DematteisUniversity of Turin VSVitalie StavilaSandia National Laboratories

Discussion

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Implication

Application of machine learning and data augmentation improves predictive accuracy of thermodynamic properties in metal hydrides, highlighting limitations in dataset diversity.

Key Points

Increased dataset size notably improves predictive accuracy for thermodynamic properties of metal hydrides, expanding the dataset from 400 to 806 entries.
Machine learning methods, including Support Vector Machines and Gradient Boosted Random Forests, were trained using enhanced datasets to assess performance.
Application of the PADRE algorithm for data augmentation shows limited benefits in predictive accuracy, especially for smaller datasets.
Limited generalizability of models across material classes emphasizes the need for diverse datasets for robust predictions in material discovery.

Cite This Study

Beltrame et al. (2025) studied this question.

synapsesocial.com/papers/68af5d75ad7bf08b1eae137a https://doi.org/10.20944/preprints202508.1673.v1

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August 22, 2025Open Access

Application of Machine Learning and Data Augmentation Algorithms in the Discovery of Metal Hydrides for Hydrogen Storage

View Full Paper

Ask AI

Bookmark

Authors

GBGiovanni BeltramePolytechnique Montréal EDErika Michela DematteisUniversity of Turin VSVitalie StavilaSandia National Laboratories

Discussion

Member takes

Implication

Application of machine learning and data augmentation improves predictive accuracy of thermodynamic properties in metal hydrides, highlighting limitations in dataset diversity.

Key Points

Increased dataset size notably improves predictive accuracy for thermodynamic properties of metal hydrides, expanding the dataset from 400 to 806 entries.
Machine learning methods, including Support Vector Machines and Gradient Boosted Random Forests, were trained using enhanced datasets to assess performance.
Application of the PADRE algorithm for data augmentation shows limited benefits in predictive accuracy, especially for smaller datasets.
Limited generalizability of models across material classes emphasizes the need for diverse datasets for robust predictions in material discovery.

Cite This Study

Beltrame et al. (2025) studied this question.

synapsesocial.com/papers/68af5d75ad7bf08b1eae137a https://doi.org/10.20944/preprints202508.1673.v1

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